A quantitative model for optimization of land use patterns and its landscape effect analysis

Reasonable planning for land use pattern resulted from economic development could effectively curb the decline in the quality of regional ecological environment and help relieve non-pollution ecological damage. However, the lack of this planning is common and, as a consequence, many areas are not effective in achieving the optimal utilization goals. In this work, we introduce a quantitative method to land use pattern planning. When formulated in mathematical terms, the problem of land use pattens subject to both box and spatial constraints results in a combinatorial optimization problem belonging to the NP-hard class. This fact and the usual dimension of the problem (regularly in the tens of thousands order) suggest the need to apply a heuristic approach. In this study we established a land use pattern optimization model based on a simulated annealing algorithm. Building upon previous work by Bos, we introduce three main innovations (a quadratic function of distance between land units, a non-symmetric matrix of compatibilities among uses, and a spatial connection constraint) that make the approach applicable for ecological purposes. When applied to solving small-size simulated problems,the results were indistinguishable from those obtained via an exact, enumerative method. A coarse-scale optimization of land use patterns in Longxing town rendered maps remarkably similar to those produced by subject area experts using a non-quantitative consensus-seeking approach. Results are encouraging since quantitative methods are regarded as fast, reliable and amenable to quick reviews and updates,they can be useful tools. Our quantitative zoning model allows for the consideration of both land aptitude and possible spatial interactions.Obviously, representation of reality is simplified; not every possible criterion for use aptitude would be easily coded for inclusion in the model. There is still ample room for the work of subject experts in defining possible uses, aptitude, compatibilities among uses, spatial constraints, etc. What the model guarantees, however, is that, given a complete input set for a target area, it will produce alternative zoning plans by modifying key input terms such as weighting coefficients and constraints. The choice of parameters will depend on the experience and ability of the group providing expert knowledge data. The proposed heuristic algorithm is efficient in producing nearoptimum values for the objective function. All solutions obtained satisfy the required constraints. However, it will usually still require several re-runs of the procedure until one or more solutions satisfying subject experts and managers are obtained. Translating the objectives and vision of different stakeholders into model parameters and constraints may prove difficult. Nonetheless, it may still be rewarding because most biases and conflicts of interest will be explicitly exposed, which could facilitate the task of finding common ground. Although our quantitative method may not be the definite answer for optimization of land use patterns, it is a fast, inexpensive, and useful tool for supporting land use planning, illuminating possible land use conflicts and, thus, hopefully, minimizing poor planning.